Ubiquity is dedicated to the future of computing and the
people who are creating it. What exactly does this mean for
readers, for contributors, and for editors soliciting and
reviewing contributions? We decided to ask the editor in chief,
Peter Denning, how he approaches the future, and how his
philosophy is reflected in the design and execution of the
Ubiquity mission. He had a surprisingly rich set of answers to
our questions. We believe his answers may be helpful for all our
readers with their own approaches to their own futures.

Ubiquity: Let me get right to the point: What is the
future?

Peter Denning: The "future" is events in a time yet to
come. Many of us would like to predict the future so that we can
take appropriate actions in the present. Most predictions are
based on some sort of recurrencesuch as a repeating cycle,
a calculable time function, or an invariant tenet about human
nature.

When I hear a prediction, my willingness to believe it depends
on the match between it and my dispositions about the future.
Some people are optimists and others pessimists. Some people
imagine the future as an already-written series of events that
will become known eventually. Others imagine the future as
malleable, the product of actions taken by people in the context
of declarations made by people.

I rate myself as an optimistic believer in the malleable
future, tempered with a good dose of prudence in anticipation of
future breakdowns. I tend to be skeptical of predictions that
depend on assumptions about what humans will declare and think.
It's easier to accept predictions of recurring events, such as
sunrises or high tides, than to accept predictions about how
people will react when the storm surge comes in. I take delight
in watching the future unfold.

Ubiquity: What do you mean by the unfolding of the
future?

Denning: Unfolding is a metaphor. When we see a bud, we
imagine what the flower will look like when it opens. It opens
gradually, eventually revealing its fullness. Unfolding is a
process of increasing degrees of opening, of incremental
revelations.

As we said a moment ago, a lot of people are interested in
predicting the future so that they can orient their present
activities accordingly. With a few exceptions we can discuss, I
think the future is inherently uncertain and unpredictable. We
are way better off if we accept the enormous uncertainty that
pervades the world and approach it with a sense of adventure and
mystery. We influence what happens next by our actions. We can
experiment to see what consequences our actions produce, and make
adjustments when we don't like the consequences. We can help the
future unfold even if we can't predict much about how it will
unfold.

There are a couple of small but important exceptions to the
unpredictability of the unfolding. One is that we can notice a
current reality that is hidden or nonobvious. We might notice the
reality by looking at data, watching conversations, or observing
practices. We then discuss the reality and its consequences in
the near terma year or two is easiest, but sometimes we
can go up to five years. Management guru Peter Drucker was a
master at this; he said that others who rated him as a good
prognosticator were wrong because all he was doing was revealing
current truths that most of them had missed.

Ubiquity: What is the other exception?

Denning: The other exception is recurrent events: We
notice that a particular event repeats and we extrapolate to its
next occurrence. Finding and exploiting recurrences in nature is
the objective of science. Engineers build tools and systems whose
behaviors can be trusted because they can be predicted accurately
by scientific laws.

The two most familiar kinds of recurrences are cycles and
functions. A cycle is an event that occurs periodically. After we
measure the length of the cycle, we can predict when the event
happens again. Predicting sunrise is an obvious example. Even if
we don't know the length of the cycle, our conviction that a
cycle exists can allow for a fuzzier, but still useful
prediction. The stock market, for example, has cycles of up and
down, but we don't know exactly when the highs and lows will
occur.

When we have a function that characterizes the data seen so
far, we can calculate future values using the function. For
example, we notice that computer chip speeds double every two
years and we predict that the speed will be eight times faster in
six years. To make this prediction, we assume that the function
itself is recurrent.

Politicians and economists are interested in historical
recurrences. These are human conditions that seem to repeat over
time, such as economic booms and busts, or cycles between war and
peace. Sometimes we can see historical recurrences and use them
to give make high probability statements about events over a
longer period, such as decades.

But mostly the things we want to know are inherently
unpredictable. They depend on too many unknown events and too
many decisions and declarations people make in the worldwide
network of conversations.

Ubiquity: What can we do in the face of all that
uncertainty?

Denning: The worst thing we can do is to become
resigned and inactive. There is an old saying that the best way
to predict the future is to invent it. In our recent book, Bob
Dunham and I discuss eight practices by which people influence
the future by bringing about change in their communities (The
Innovator's Way, MIT Press, 2010). The process of inventing
the future is one we can become skilled at. Much of the skill is
coping gracefully with the breakdowns and surprises that we will
inevitably encounter. I call this "blending with the
unfolding."

Ubiquity: Why is prediction a concern for people?

Denning: Uncertainty produces discomfort and
disruption. Most of us would rather keep improving our lots in
life and not have to put up with disruption. We see technology as
progressive, always pulling the world in a better direction. For
example, investors would like to predict the stock market.
Homeowners would like to know if they are buying in an
appreciating neighborhood. College students would like to know
which fields will be hot after they graduate. Professionals would
like to know what they should learn that will be useful when new
technologies become mainstream.

I think that trying to predict the future is a losing
proposition. Our track record is absolutely miserable. With few
exceptionsnotably predicting events with definite
recurrenceswe get it wrong. That's why science has such an
emphasis on reproducibilitypeople love it that well
validated scientific phenomena can give reliable predictions that
can reduce risks. But in every domain of politics, economics,
business, and even science, numerous macro and micro events defy
prediction.

Ubiquity: But we see loads of technology projections.
Some of them, like Moore's Law look so spot on that entire
industries rely on them, and enable futurists like Ray Kurzweil
to predict dramatic changes to humanity within the next
generation or two.

Denning: I agree that there are many technology
projections. Let's take a look at a few of the more common ones
and see how well grounded they are.

In the early 1990s, Internet pundits promoted a pile of
predictions that led to the business disasters of
20012002, known as the "dot com bust." John Seely Brown
and Paul Duguid wrote a book analyzing why the main predictions
went wrong (The Social Life of Information, Harvard
Business School Press, 2002). The main predictions they
considered included disappearance of libraries, universities,
newspapers, and physical workplaces. Some of these predictions
came from prominent peoplefor example, Peter Drucker was
among those predicting the end of the university. None of those
predictions came true. What did happen is that those institutions
changed and adapted, but they are all still very much with us.
All the predictions were based on extrapolations of technology
trends discernable around 1990. They failed because technology is
only a part of a social system. The prognosticators did not
consider how the structure and conceptual understanding of the
system might change. A change happens only when technology,
structure, and concept all change together. The Internet
predictions assumed that the technology drives the other two. In
reality, the other two pushed back and changed the technology. In
the social system, the technology and its adoption evolved in a
different way from the technology extrapolation.

The notion that technology drives (causes) change is very much
with us today. We like to say that the Internet technology drove
a huge number of changes in the world, including today political
and economic changes. This notion is, in my view mistaken. Did
the Internet really cause the change? Or did it
accompany the change? Might not the desire of people to
communicate more rapidly and do business with more customers have
inclined investors to invest in the Internet technology and
deploy it widely?

Ubiquity: What about Moore's Law? Isn't that a perfect
example of technology driving change in society?

Denning: Moore's law began with Gordon Moore's
observation in 1965 that the number of transistors on a chip
doubles every two years at the same chip price. Over seven years
this produces a 10X (10-fold) speedup. The 10X improvement makes
previously expensive computational methods much cheaper and
induces people to try new methods that bring them better value.
Notice that the changes come from people as they adopt the new
technology. The technology is not doing the driving; the people
are. This is very important. The changes accompanying Moore's Law
are happening in social systems. We can come back to this later
if you like.

No law of nature mandates chip doublings every two years. Many
leaders in the chip industry have made it their business
objective to double the speed of their chips every two years.
Might Moore's law be the manifestation of policy decisions made
by business leaders?

Ray Kurzweil has argued that a similar doubling rule can be
observed in the four information technology generations that
preceded silicon chips. He believes that when Moore's Law runs
out for siliconwhen wires become less than an atom
widesome other technology will step in to continue the
process. Today, for example, we see multi-core chips continuing
the doubling trend by placing many processors on the same wafer
so that they can all proceed in parallel.

Ubiquity: Ray Kurzweil is so certain that Moore's Law
will persist through future generations of information
technology, that he predicts a "singularity" sometime around
2030. Aren't his predictions founded on a well-established
law?

Denning: It depends on what you mean by well
established. It's not a scientific law. It manifests the
aggregate effects of many business decisions. Kurzweil may be
right about the technology, but people's reactions and
adaptations to the technology are harder to gauge. For example,
I've heard a lot of people say they see Kurzweil's predictions
but don't believe the proposed singularity consequences. Some
people believe that with the coming changes of bionic replacement
nano-parts, along with brain and other neural implants, we will
gradually transform our children and grandchildren into the new
beings that Kurzweil says are probable. In other words, we would
not create new superintelligences, we would become
them ourselves. No one really knows. AI researchers have a
marvelous adventure as they explore these things.

Ubiquity: What about other attempts at long term
predictions? Do any of them work?

Denning: In 1892 there was a great exhibition in
Chicago celebrating the 400th anniversary of Columbus discovering
America. The American Press Association invited 74 leading
authors, journalists, industrialists, business leaders,
engineers, social critics, lawyers, politicians, religious
leaders, and other luminaries of the day to give their forecasts
of the world 100 years hence, in 1992. In 1992 Dave Walter
(Today Then, American World Geographic Publishing, 1992)
published a book that reprinted those old essays so that we could
all see how accurate they were. All but four were completely
wrong. The best Walter could say is that the essays tell us more
about the writers and the context of their day than about the
future. Many predicted the wars would end, plagues and
pestilences would be conquered, social classes would be erased,
unemployment and poverty would disappear. Many believed that
pneumatic tubes would replace buses and trains as primary
transportation within cities. Others believed that high speed
railways would be the primary method of transport across all of
North, Central, and South America. Only one thought that air
transport would be of any value, and even he thought the value
would be primarily military. No one foresaw radio, television,
computers, or the Internet. No one foresaw any of the major
discoveries of physics during the 20th century. If there is a
lesson to be learned from Walter's study, it is that whatever we
predict for the long term will almost certainly be wrong.

Ubiquity: You make a good point about changes happening
because people adopt them. But before people can adopt anything,
someone has to invent it. Isn't important for technology progress
to identify and reward inventors and provide incentives for more
people to become inventors? Can we do more to identify great
inventions early?

Denning: I am very skeptical that many innovations were
the consequence of ideas of inventors who created early ideas. In
our book, we call that the "Invention Myth"; others call it the
"Eureka Myth." It is easy to look backwards and see a chain of
connections (e.g., literature citations) from someone's ideas to
the present. We try to locate the earliest person to propose the
idea and then give that person special recognitions for being the
original source of the idea behind today's innovation. But a
chain of connections is not a chain of causation. In our
research, we discovered that, almost all the time, the inventors
of ideas are not the ones who brought the idea into practice and
adoption. Fortunately for the inventors, there are numerous
innovators. Something seems out of balance to me if we give more
recognition to inventors than to innovators. We are not hurting
for ideas as much as we are for people skilled in bringing about
adoption of ideas.

The fundamental problem is that the judgment whether an
invention is great is rendered many years later by those who are
immersed in the innovations that followed. It is difficult to
tell at the time of an invention whether it will be great.

Ubiquity: What about Andy Grove's idea of the 10X
technology and inflection point? Doesn't that give a method of
prediction?

Denning: Andy Grove, the former chairman of Intel,
wrote a book about how he steered Intel (Only the Paranoid
Survive, Doubleday, 1996). He observed that if someone had a
prototype of a new technology that looked like it could do a
familiar and pervasive job 10X (10-fold) faster or cheaper, it
stood a good chance of being a disruptive technology. Grove did
not want Intel to be surprised by disruptive technologies of
competitors. He therefore invested in research that would see if
Intel could realize the 10X improvement in a technology it
controlled. Grove did not make technology predictions per se. He
created options for the company in case any potentially
disruptive technology actually became a threat. Many of his
experiments led nowhere, but some paid off handsomely for the
company.

Ubiquity: What about chaos theory? Can it predict the
future?

Denning: Many things are chaotic and unpredictable.
We'd like to know when the next earthquake will happen. Or
whether a nuclear power plant can withstand a 9.0 shock. Or who
will prevail in the Middle East. Or how to get the national
economy to grow. Or whether a social networking company will
succeed or go bust. Or who will be our competitors when our
product is ready for market. Or whether 2012 be a disaster year
for the planet? On and on.

Santa Fe Institute was formed to explore chaotic phenomena
mathematically and see if there are any exploitable recurrences.
They discovered some interesting things including power laws,
sudden phase transitions, and scale-free systems. But these have
not helped much with making predictions. The mean and standard
deviation of a power law distribution are infinite or undefined,
meaning you cannot set confidence intervals on predictions. The
mathematical model says Internet is scale free but in reality
engineers design in redundancy and that falsifies the model's
predictions on failure probabilities. An earthquake is an example
of a cascade phase change, but all that tells us is that we can't
know at that moment it starts how long it will last or how much
energy it will release; we'll know a few minutes or hours
later.

So I don't think chaos theory is very helpful for most of the
human systems whose future we would love to know.

Ubiquity: We're not done exploring prediction methods
yet. Futurists use trend extrapolations and scenarios. How good
are these methods?

Denning: Trend extrapolation means to find some
variable in the data that can be described as a time function,
then use that function to predict a future value. I noted earlier
that these become increasingly unreliable with distance into the
future; they are at best good for a year or two before the
environment changes too much. And of course they are no good at
all if the environment undergoes a disruptive change, so that the
function no longer applies.

Scenarios are short stories that depict a future situation and
explore the consequences. Scenarios have been very helpful to
help people understand their own reactions to various
possibilities. If futurists discover scenarios they do not like,
they inquire into policies that could be implemented in the
present to make that future less likely. And of course they favor
present policies that make good futures more likely. Since there
are no guarantees that any of the policies leads to the desired
outcomes, scenarios are not really a method of prediction. They
are a method of evaluating reactions to possible worlds.

Ubiquity: Finally, let me ask about "learning from
failures," about which there has been much discussion of
late.

Denning: Yes, Google has publicized its company
practice of encouraging people to fail early and often, and learn
from the failures what might work. Years before Google existed,
Peter Drucker pointed out that failures can be sources of
innovations.

The key idea is not failure, but learning. The process of
embracing uncertainty and adventuring in the mysteries of the
world is a learning process. We cannot learn if we do not try.
When something we try fails, we seek to understand what made it
fail and modify our future behavior when we try again.

I don't see learning from failures to be a prediction method,
but a practice for blending with the unfolding world.

Ubiquity: Let's bring all this back to Ubiquity. What
does it say about the kinds of things Ubiquity publishes?
What can readers take away from what you have said?

Denning: In Ubiquity, about half our articles
are commentaries and the other half interviews. The commentaries
give authors the opportunity to expose truths about the world
today and explore the near term consequences. All the
commentaries are on file in the ACM Digital Library, we hope that
future authors will review some of them to see how well those
authors did with their extrapolations.

The interviews give us firsthand accounts from people who are
engaging with the future as an unfolding adventure in the
mysteries of the world. We are particularly interested in how
they cope with the breakdowns and surprises they encounter. The
how may be useful to other readers.

In the past year, we instituted a new feature, the
Ubiquity symposium, to allow a group of participants to
explore a proposition. We intensely dislike the
"point-counterpoint debate" formulation popular in many magazines
and talk shows. That formula makes it seem that every proposition
has only two sides and one must be right. It is a bad way to
grapple with the mysteries of life. Our symposia, instead,
encourage inquiry and exploration. We hope our symposia are
learning experiences for readers, and that they will gain a
greater understanding of a difficult topic by seeing how others
are grappling with it.

Our intention is to help readers face the uncertainties of the
future and see that they can develop effective practices for
coping with them. This is what I mean when I say the end (goal)
of Ubiquity is the future.

Ubiquity: Thank you.

Denning: You're welcome.

Author

Brian Branagan has worked in the field of software test
management and quality engineering for more than 20 years in
Fortune 500 companies such as Adobe Systems, Getty Images, and
RealNetworks. He is currently the Director of Test Engineering at
F5 Networks. Brian's focus areas include risk management for
complex systems and software project management
methodologies.

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